code: def __init__(self, n_output):
      super(DiscBlock1, self).__init__()
  
      self.conv1 = nn.Conv2d(3, n_output, 3, padding=(3 - 1) // 2)
      self.conv2 = ConvMeanPool(
          n_output, n_output, 1, kaiming_init=True
      )
      self.conv_shortcut = MeanPoolConv(
          3, n_output, 1, kaiming_init=False
      )
  
      # Weight initialization:
      nn.init.kaiming_uniform_(
          self.conv1.weight, mode="fan_in", nonlinearity="relu"
      )
  
      self.model = nn.Sequential(
          self.conv1, nn.ReLU(inplace=True), self.conv2
      )
  
imports: from torch import nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new DiscBlock1 object from inherited base classes: nn with example: obj = DiscBlock1(n_output)